refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-pose-estimation
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title: Pose Estimation
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Human Pose Estimation, HPE, Keypoint Detection]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [computer-vision, pose-estimation, deep-learning, keypoints]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: pytorch, mmpose, mediapipe
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---
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# Pose Estimation
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## 매 한 줄
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> **"매 image/video에서 인체 keypoints (joints) 위치 detection."**. OpenPose (2017)가 multi-person bottom-up을 popularize, MediaPipe로 mobile real-time, 2024-2025 ViTPose / SAM-style transformer가 SOTA.
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## 매 핵심
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### 매 두 가지 paradigm
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- **Top-down**: detect person bbox → crop → keypoint regression. 매 정확, slow with crowd.
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- **Bottom-up**: keypoints first → group into persons (PAF / associative embedding). 매 fast at scale.
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- **Single-stage** (modern): YOLO-Pose, ED-Pose — detection + keypoints joint.
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### 매 표현 방식
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- **2D keypoints**: (x, y, confidence) — COCO 17 keypoints standard.
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- **3D pose**: (x, y, z) — single image lift 또는 multi-view.
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- **SMPL / mesh**: full body parametric model — VIBE, HMR, 4D-Humans.
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### 매 응용
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1. AR/VR avatar driving (Meta Quest, Apple Vision Pro).
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2. Fitness coaching (form correction).
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3. Sports analytics (gait, biomechanics).
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4. Animation mocap markerless.
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5. Surveillance / fall detection.
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## 💻 패턴
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### MediaPipe (real-time, on-device)
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```python
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import mediapipe as mp
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import cv2
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mp_pose = mp.solutions.pose
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pose = mp_pose.Pose(model_complexity=1, min_detection_confidence=0.5)
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cap = cv2.VideoCapture(0)
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while cap.isOpened():
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ok, frame = cap.read()
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if not ok: break
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results = pose.process(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
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if results.pose_landmarks:
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for lm in results.pose_landmarks.landmark:
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print(lm.x, lm.y, lm.visibility)
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```
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### MMPose (research, ViTPose backbone)
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```python
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from mmpose.apis import MMPoseInferencer
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inferencer = MMPoseInferencer(pose2d='vitpose-h')
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result = next(inferencer('image.jpg', show=False))
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keypoints = result['predictions'][0][0]['keypoints'] # (17, 2)
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scores = result['predictions'][0][0]['keypoint_scores']
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```
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### YOLO-Pose (Ultralytics, single-stage)
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```python
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from ultralytics import YOLO
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model = YOLO('yolo11n-pose.pt')
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results = model('image.jpg')
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for r in results:
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kpts = r.keypoints.xy # (n_persons, 17, 2)
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conf = r.keypoints.conf
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```
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### 3D lift (VideoPose3D-style)
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```python
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import torch
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# 2D (T, 17, 2) -> 3D (T, 17, 3) via temporal CNN
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class TemporalLift(torch.nn.Module):
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def __init__(self, n_kpts=17, ch=1024):
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super().__init__()
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self.expand = torch.nn.Conv1d(n_kpts*2, ch, 3, padding=1)
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self.blocks = torch.nn.Sequential(*[
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torch.nn.Sequential(
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torch.nn.Conv1d(ch, ch, 3, padding=1, dilation=d),
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torch.nn.BatchNorm1d(ch), torch.nn.ReLU()
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) for d in (3, 9, 27)
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])
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self.head = torch.nn.Conv1d(ch, n_kpts*3, 1)
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def forward(self, x): # x: (B, T, 17, 2)
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B, T = x.shape[:2]
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x = x.reshape(B, T, -1).transpose(1, 2)
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return self.head(self.blocks(self.expand(x))).transpose(1, 2).reshape(B, T, -1, 3)
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```
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### COCO keypoint metric (OKS / mAP)
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```python
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from pycocotools.coco import COCO
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from pycocotools.cocoeval import COCOeval
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gt = COCO('person_keypoints_val2017.json')
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dt = gt.loadRes('predictions.json')
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e = COCOeval(gt, dt, 'keypoints')
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e.evaluate(); e.accumulate(); e.summarize()
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# AP @ OKS=.50:.95 — 표준 metric
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```
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### SMPL mesh recovery (4D-Humans / HMR2)
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```python
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from hmr2.models import load_hmr2
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model, model_cfg = load_hmr2('logs/checkpoints/epoch=35.ckpt')
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out = model(image_tensor)
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verts = out['pred_vertices'] # (B, 6890, 3)
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betas = out['pred_smpl_params']['betas']
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pose = out['pred_smpl_params']['body_pose']
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Mobile / web real-time | MediaPipe Pose |
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| Highest accuracy single image | ViTPose-H (MMPose) |
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| Multi-person crowd | YOLO-Pose / ED-Pose (single-stage) |
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| 3D from monocular video | 4D-Humans / WHAM |
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| Animation mocap | SMPL / SMPL-X based |
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| Edge device < 10ms | MoveNet Lightning, RTMPose-tiny |
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**기본값**: 2D는 RTMPose, 3D mesh는 4D-Humans.
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## 🔗 Graph
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- 부모: [[Computer_Vision]] · [[Deep Learning]]
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- 변형: [[MediaPipe]]
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- Adjacent: [[Object_Detection]] · [[Keypoint_Detection]]
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## 🤖 LLM 활용
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**언제**: vision-action pipeline 의 input feature, fitness/AR app, mocap automation.
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**언제 X**: facial keypoints는 face-specific model (MediaPipe Face Mesh, dlib), hand는 MediaPipe Hands.
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## ❌ 안티패턴
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- **Top-down without bbox tracking**: 매 frame redetect — temporal jitter 매 심각. ByteTrack 결합.
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- **2D regression direct (x,y) without heatmap**: 매 lower accuracy. Heatmap supervision 매 표준.
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- **3D from single 2D pose**: depth ambiguity — temporal context 또는 multi-view 필요.
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- **Ignoring camera intrinsics for 3D**: 매 metric scale wrong.
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## 🧪 검증 / 중복
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- Verified (MMPose docs, Ultralytics YOLO11-pose, MediaPipe docs, COCO keypoint benchmark).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — pose estimation paradigms + modern stack (ViTPose, YOLO-Pose, 4D-Humans) |
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